
R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
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Platform: x86_64-apple-darwin10.8.0 (64-bit)

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> # for NO!RE74
> 
> rm(list=ls())
> library(Matching)
Loading required package: MASS
## 
##  Matching (Version 4.8-3.4, Build Date: 2013/10/28)
##  See http://sekhon.berkeley.edu/matching for additional documentation.
##  Please cite software as:
##   Jasjeet S. Sekhon. 2011. ``Multivariate and Propensity Score Matching
##   Software with Automated Balance Optimization: The Matching package for R.''
##   Journal of Statistical Software, 42(7): 1-52. 
##

> 
> # This sources in a function that creates the right model for propensity score matching & MatchBalance
> 
> load(file="rep.sourcecode.nore74cps1.RData")
> 
> X.Bal <- as.matrix(cbind(
+                          foo$age,
+                          foo$educ,
+                          foo$black,
+                          foo$hispan,
+                          foo$married,
+                          foo$nodegree,
+                          I(foo$re75/1000),
+                          I((foo$re75/1000)^2),
+                          I(foo$hispan*foo$educ),
+                          I((foo$age)^2),
+                          I((foo$educ)^2),
+                          I((foo$black)^2),
+                          I((foo$hispan)^2),
+                          I((foo$married)^2),
+                          I((foo$nodegree)^2),
+                          I((foo$re75/1000)^2),
+                          I(foo$age*foo$educ),
+                          I(foo$age*foo$black),
+                          I(foo$age*foo$hispan),
+                          I(foo$age*foo$married),    
+                          I(foo$age*foo$nodegree),
+                          I(foo$age*(foo$re75/1000)),
+                          I(foo$educ*foo$black),
+                          I(foo$educ*foo$married),    
+                          I(foo$educ*foo$nodegree),
+                          I(foo$educ*(foo$re75/1000)),
+                          I(foo$black*foo$hispan),
+                          I(foo$black*foo$married),
+                          I(foo$black*foo$nodegree),
+                          I(foo$black*(foo$re75/1000)),
+                          I(foo$hispan*foo$married),
+                          I(foo$hispan*foo$nodegree),
+                          I(foo$hispan*(foo$re75/1000)),
+                          I(foo$married*foo$nodegree),
+                          I(foo$married*(foo$re75/1000)),
+                          I(foo$nodegree*(foo$re75/1000))                        
+                          ))
> X <- X.Bal[,1:9]
> 
> sv <- c(9.240000e+02, 2.110000e+02, 5.500000e+02, 7.940000e+02, 4.660000e+02, 3.190000e+02, 1.030000e+02, 8.820000e+02, 2.430000e+02)
> 
> genout <- GenMatch(Tr = foo$treat, X = X, BalanceMatrix = X.Bal,
+                    starting.values=sv,
+                    pop.size = 1, wait.generations = 1,
+                    max.generations = 1,
+                    hard.generation.limit=TRUE)
Loading required package: rgenoud
Loading required package: parallel
##  rgenoud (Version 5.7-12, Build Date: 2013-06-28)
##  See http://sekhon.berkeley.edu/rgenoud for additional documentation.
##  Please cite software as:
##   Walter Mebane, Jr. and Jasjeet S. Sekhon. 2011.
##   ``Genetic Optimization Using Derivatives: The rgenoud package for R.''
##   Journal of Statistical Software, 42(11): 1-26. 
##



Sun Nov 17 20:14:17 2013
Domains:
 0.000000e+00   <=  X1   <=    1.000000e+03 
 0.000000e+00   <=  X2   <=    1.000000e+03 
 0.000000e+00   <=  X3   <=    1.000000e+03 
 0.000000e+00   <=  X4   <=    1.000000e+03 
 0.000000e+00   <=  X5   <=    1.000000e+03 
 0.000000e+00   <=  X6   <=    1.000000e+03 
 0.000000e+00   <=  X7   <=    1.000000e+03 
 0.000000e+00   <=  X8   <=    1.000000e+03 
 0.000000e+00   <=  X9   <=    1.000000e+03 
NOTE: population size is not an even number
      increasing population size by 1

Data Type: Floating Point
Operators (code number, name, population) 
	(1) Cloning........................... 	1
	(2) Uniform Mutation.................. 	0
	(3) Boundary Mutation................. 	0
	(4) Non-Uniform Mutation.............. 	0
	(5) Polytope Crossover................ 	0
	(6) Simple Crossover.................. 	0
	(7) Whole Non-Uniform Mutation........ 	0
	(8) Heuristic Crossover............... 	0
	(9) Local-Minimum Crossover........... 	0

HARD Maximum Number of Generations: 1
Maximum Nonchanging Generations: 1
Population size       : 2
Convergence Tolerance: 1.000000e-03

Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
Not Checking Gradients before Stopping.
Using Out of Bounds Individuals.

Maximization Problem.
GENERATION: 0 (initializing the population)
Lexical Fit..... 2.400327e-01  2.400327e-01  2.674471e-01  2.768660e-01  2.778772e-01  2.778772e-01  2.825584e-01  3.160763e-01  3.173126e-01  3.173126e-01  3.173126e-01  3.173126e-01  3.173126e-01  3.173126e-01  4.044378e-01  4.515740e-01  4.642311e-01  5.272994e-01  5.787371e-01  5.814933e-01  5.888980e-01  5.910744e-01  5.932036e-01  6.549393e-01  7.457885e-01  7.930082e-01  8.091408e-01  8.091408e-01  9.038175e-01  9.038175e-01  9.038175e-01  9.038175e-01  9.069047e-01  9.241456e-01  9.394425e-01  9.458530e-01  9.659295e-01  9.672918e-01  9.934606e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 2, #Total UniqueCount: 2
var 1:
best............ 9.240000e+02
mean............ 6.669318e+02
variance........ 6.608405e+04
var 2:
best............ 2.110000e+02
mean............ 2.286658e+02
variance........ 3.120799e+02
var 3:
best............ 5.500000e+02
mean............ 5.063113e+02
variance........ 1.908707e+03
var 4:
best............ 7.940000e+02
mean............ 8.712880e+02
variance........ 5.973427e+03
var 5:
best............ 4.660000e+02
mean............ 3.823608e+02
variance........ 6.995521e+03
var 6:
best............ 3.190000e+02
mean............ 3.233807e+02
variance........ 1.919051e+01
var 7:
best............ 1.030000e+02
mean............ 3.030465e+02
variance........ 4.001861e+04
var 8:
best............ 8.820000e+02
mean............ 5.868641e+02
variance........ 8.710521e+04
var 9:
best............ 2.430000e+02
mean............ 2.041180e+02
variance........ 1.511810e+03

GENERATION: 1
Lexical Fit..... 2.400327e-01  2.400327e-01  2.674471e-01  2.768660e-01  2.778772e-01  2.778772e-01  2.825584e-01  3.160763e-01  3.173126e-01  3.173126e-01  3.173126e-01  3.173126e-01  3.173126e-01  3.173126e-01  4.044378e-01  4.515740e-01  4.642311e-01  5.272994e-01  5.787371e-01  5.814933e-01  5.888980e-01  5.910744e-01  5.932036e-01  6.549393e-01  7.457885e-01  7.930082e-01  8.091408e-01  8.091408e-01  9.038175e-01  9.038175e-01  9.038175e-01  9.038175e-01  9.069047e-01  9.241456e-01  9.394425e-01  9.458530e-01  9.659295e-01  9.672918e-01  9.934606e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 0, #Total UniqueCount: 2
var 1:
best............ 9.240000e+02
mean............ 6.669318e+02
variance........ 6.608405e+04
var 2:
best............ 2.110000e+02
mean............ 2.286658e+02
variance........ 3.120799e+02
var 3:
best............ 5.500000e+02
mean............ 5.063113e+02
variance........ 1.908707e+03
var 4:
best............ 7.940000e+02
mean............ 8.712880e+02
variance........ 5.973427e+03
var 5:
best............ 4.660000e+02
mean............ 3.823608e+02
variance........ 6.995521e+03
var 6:
best............ 3.190000e+02
mean............ 3.233807e+02
variance........ 1.919051e+01
var 7:
best............ 1.030000e+02
mean............ 3.030465e+02
variance........ 4.001861e+04
var 8:
best............ 8.820000e+02
mean............ 5.868641e+02
variance........ 8.710521e+04
var 9:
best............ 2.430000e+02
mean............ 2.041180e+02
variance........ 1.511810e+03

Solution Lexical Fitness Value:
2.400327e-01  2.400327e-01  2.674471e-01  2.768660e-01  2.778772e-01  2.778772e-01  2.825584e-01  3.160763e-01  3.173126e-01  3.173126e-01  3.173126e-01  3.173126e-01  3.173126e-01  3.173126e-01  4.044378e-01  4.515740e-01  4.642311e-01  5.272994e-01  5.787371e-01  5.814933e-01  5.888980e-01  5.910744e-01  5.932036e-01  6.549393e-01  7.457885e-01  7.930082e-01  8.091408e-01  8.091408e-01  9.038175e-01  9.038175e-01  9.038175e-01  9.038175e-01  9.069047e-01  9.241456e-01  9.394425e-01  9.458530e-01  9.659295e-01  9.672918e-01  9.934606e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  

Parameters at the Solution:

 X[ 1] :	9.240000e+02
 X[ 2] :	2.110000e+02
 X[ 3] :	5.500000e+02
 X[ 4] :	7.940000e+02
 X[ 5] :	4.660000e+02
 X[ 6] :	3.190000e+02
 X[ 7] :	1.030000e+02
 X[ 8] :	8.820000e+02
 X[ 9] :	2.430000e+02

Solution Found Generation 1
Number of Generations Run 1

Sun Nov 17 20:14:17 2013
Total run time : 0 hours 0 minutes and 0 seconds
> 
> m1 <- Match(Y=foo$re78, Tr=foo$treat, X=X, Weight.matrix=genout)
> summary(m1)

Estimate...  185.32 
AI SE......  681.59 
T-stat.....  0.2719 
p.val......  0.7857 

Original number of observations..............  16289 
Original number of treated obs...............  297 
Matched number of observations...............  297 
Matched number of observations  (unweighted).  397 

> 
> 
> 
> proc.time()
   user  system elapsed 
  3.053   0.167   3.254 
